Software applications often display content to users that might be helpful in using the application, including links to knowledge base articles, discussion forums, or help centers. Unfortunately, when there is an abundance of help content (e.g., knowledge bases can have hundreds to thousands of articles), to provide relevant content, application designers must either 1) manually select which content appears in which places in an application, 2) provide a search option to allow users to find relevant content, or 3) use some computer-based content selection method to decide which content is most relevant to display in particular situations.
In the case of automated content selection, there is a wide range of information that might be taken into account to decide what content to display. Prior art teaches of methods that segregate software applications into different screens, providing content that is specific to the screen, requiring each piece of content to be annotated with the screen to which it applies. Other prior art teaches of methods that track which content is accessed most frequently by application users, selecting only the most viewed content for display. Other methods track a specific individual user's activity, using aspects of the application that the user has engaged with to identify content that is related to those aspects.
There exists a need for methods that can combine existing prior art with the data about all of the users of an application to select the most relevant content.